cacomp: Correspondance Analysis

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Correspondance Analysis

Description

'cacomp' performs correspondence analysis on a matrix or Seurat/SingleCellExperiment object and returns the transformed data.

'cacomp.seurat' performs correspondence analysis on an assay from a Seurat container and stores the standardized coordinates of the columns (= cells) and the principal coordinates of the rows (= genes) as a DimReduc Object in the Seurat container.

'cacomp.SingleCellExperiment' performs correspondence analysis on an assay from a SingleCellExperiment and stores the standardized coordinates of the columns (= cells) and the principal coordinates of the rows (= genes) as a matrix in the SingleCellExperiment container.

Usage

cacomp(
  obj,
  coords = TRUE,
  princ_coords = 3,
  python = FALSE,
  dims = NULL,
  top = 5000,
  inertia = TRUE,
  rm_zeros = TRUE,
  residuals = "pearson",
  cutoff = NULL,
  clip = TRUE,
  ...
)

## S4 method for signature 'matrix'
cacomp(
  obj,
  coords = TRUE,
  princ_coords = 3,
  python = FALSE,
  dims = NULL,
  top = 5000,
  inertia = TRUE,
  rm_zeros = TRUE,
  residuals = "pearson",
  cutoff = NULL,
  clip = TRUE,
  ...
)

## S4 method for signature 'dgCMatrix'
cacomp(
  obj,
  coords = TRUE,
  princ_coords = 3,
  python = FALSE,
  dims = NULL,
  top = 5000,
  inertia = TRUE,
  rm_zeros = TRUE,
  residuals = "pearson",
  cutoff = NULL,
  clip = TRUE,
  ...
)

## S4 method for signature 'Seurat'
cacomp(
  obj,
  coords = TRUE,
  princ_coords = 3,
  python = FALSE,
  dims = NULL,
  top = 5000,
  inertia = TRUE,
  rm_zeros = TRUE,
  residuals = "pearson",
  cutoff = NULL,
  clip = TRUE,
  ...,
  assay = Seurat::DefaultAssay(obj),
  slot = "counts",
  return_input = FALSE
)

## S4 method for signature 'SingleCellExperiment'
cacomp(
  obj,
  coords = TRUE,
  princ_coords = 3,
  python = FALSE,
  dims = NULL,
  top = 5000,
  inertia = TRUE,
  rm_zeros = TRUE,
  residuals = "pearson",
  cutoff = NULL,
  clip = TRUE,
  ...,
  assay = "counts",
  return_input = FALSE
)

Arguments

obj	A numeric matrix or Seurat/SingleCellExperiment object. For sequencing a count matrix, gene expression values with genes in rows and samples/cells in columns. Should contain row and column names.
coords	Logical. Indicates whether CA standard coordinates should be calculated.
princ_coords	Integer. Number indicating whether principal coordinates should be calculated for the rows (=1), columns (=2), both (=3) or none (=0).
python	A logical value indicating whether to use singular-value decomposition from the python package torch. This implementation dramatically speeds up computation compared to 'svd()' in R when calculating the full SVD. This parameter only works when dims==NULL or dims==rank(mat), where caculating a full SVD is demanded.
dims	Integer. Number of CA dimensions to retain. Default NULL (keeps all dimensions).
top	Integer. Number of most variable rows to retain. Set NULL to keep all.
inertia	Logical. Whether total, row and column inertias should be calculated and returned.
rm_zeros	Logical. Whether rows & cols containing only 0s should be removed. Keeping zero only rows/cols might lead to unexpected results.
residuals	character string. Specifies which kind of residuals should be calculated. Can be "pearson" (default), "freemantukey" or "NB" for negative-binomial.
cutoff	numeric. Residuals that are larger than cutoff or lower than -cutoff are clipped to cutoff.
clip	logical. Whether residuals should be clipped if they are higher/lower than a specified cutoff
...	Other parameters
assay	Character. The assay from which extract the count matrix for SVD, e.g. "RNA" for Seurat objects or "counts"/"logcounts" for SingleCellExperiments.
slot	character. The slot of the Seurat assay. Default "counts".
return_input	Logical. If TRUE returns the input (SingleCellExperiment/Seurat object) with the CA results saved in the reducedDim/DimReduc slot "CA". Otherwise returns a "cacomp". Default FALSE.

Details

The calculation is performed according to the work of Michael Greenacre. Singular value decomposition can be performed either with the base R function 'svd' or preferably by the faster pytorch implementation (python = TRUE). When working with large matrices, CA coordinates and principal coordinates should only be computed when needed to save computational time.

Value

Returns a named list of class "cacomp" with components U, V and D: The results from the SVD. row_masses and col_masses: Row and columns masses. top_rows: How many of the most variable rows were retained for the analysis. tot_inertia, row_inertia and col_inertia: Only if inertia = TRUE. Total, row and column inertia respectively.

If return_imput = TRUE with Seurat container: Returns input obj of class "Seurat" with a new Dimensional Reduction Object named "CA". Standard coordinates of the cells are saved as embeddings, the principal coordinates of the genes as loadings and the singular values (= square root of principal intertias/eigenvalues) are stored as stdev. To recompute a regular "cacomp" object without rerunning cacomp use 'as.cacomp()'.

If return_input =TRUE for SingleCellExperiment input returns a SingleCellExperiment object with a matrix of standardized coordinates of the columns in reducedDim(obj, "CA"). Additionally, the matrix contains the following attributes: "prin_coords_rows": Principal coordinates of the rows. "singval": Singular values. For the explained inertia of each principal axis calculate singval^2. "percInertia": Percent explained inertia of each principal axis. To recompute a regular "cacomp" object from a SingleCellExperiment without rerunning cacomp use 'as.cacomp()'.

References

Greenacre, M. Correspondence Analysis in Practice, Third Edition, 2017.

Examples

# Simulate scRNAseq data.
cnts <- data.frame(cell_1 = rpois(10, 5),
                   cell_2 = rpois(10, 10),
                   cell_3 = rpois(10, 20))
rownames(cnts) <- paste0("gene_", 1:10)
cnts <- as.matrix(cnts)

# Run correspondence analysis.
ca <- cacomp(obj = cnts, princ_coords = 3, top = 5)

###########
# Seurat  #
###########
library(Seurat)
set.seed(1234)

# Simulate counts
cnts <- mapply(function(x){rpois(n = 500, lambda = x)},
                     x = sample(1:20, 50, replace = TRUE))
rownames(cnts) <- paste0("gene_", 1:nrow(cnts))
colnames(cnts) <- paste0("cell_", 1:ncol(cnts))

# Create Seurat object
seu <- CreateSeuratObject(counts = cnts)

# Run CA and save in dim. reduction slot
seu <- cacomp(seu, return_input = TRUE, assay = "RNA", slot = "counts")

# Run CA and return cacomp object
ca <- cacomp(seu, return_input = FALSE, assay = "RNA", slot = "counts")

########################
# SingleCellExperiment #
########################
library(SingleCellExperiment)
set.seed(1234)

# Simulate counts
cnts <- mapply(function(x){rpois(n = 500, lambda = x)},
               x = sample(1:20, 50, replace = TRUE))
rownames(cnts) <- paste0("gene_", 1:nrow(cnts))
colnames(cnts) <- paste0("cell_", 1:ncol(cnts))
logcnts <- log2(cnts + 1)

# Create SingleCellExperiment object
sce <- SingleCellExperiment(assays=list(counts=cnts, logcounts=logcnts))

# run CA and save in dim. reduction slot.
sce <- cacomp(sce, return_input = TRUE, assay = "counts") # on counts
sce <- cacomp(sce, return_input = TRUE, assay = "logcounts") # on logcounts

# run CA and return cacomp object.
ca <- cacomp(sce, return_input = FALSE, assay = "counts")

ClemensKohl/APL documentation built on Dec. 31, 2022, 8:13 a.m.